Prompt optimization for generative machine learning models

Abstract

Aspects of the present disclosure involve prompt optimization for language processing machine learning models. Candidate prompts are evaluated on a task dataset by providing each candidate prompt to a language processing machine learning model to produce outputs and scoring the outputs to obtain fitness scores. A lowest-fitness candidate prompt is identified, and a predator vector is injected into a set of predator vectors based on a prompt embedding of the lowest-fitness candidate prompt. For a given candidate prompt a target embedding is computed based on applying a repulsion term away from the predator vectors. Edited prompts are generated using discrete textual edits, and an edited prompt having an embedding closest to the target embedding is selected as an updated candidate prompt. An output is generated using the language model based on the updated candidate prompt.